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    Inverse View

    It is not the case that Bayesian logic of evidential support does not require precise values for prior probabilities

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
    ?
    • 1.Prior probabilities assigned to hypotheses reflect substantive theoretical commitments that cannot be reduced to mere bounds or intervals.
      ?

      Think about whether this reason is strong or weak

    • 2.When priors encode genuine disagreement between scientific paradigms, no amount of shared evidence forces convergence within finite data sets.
      ?

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    • 3.Cox's theorem and de Finetti's representation theorem jointly entail that coherent degrees of belief must be precise, not imprecise intervals.
      ?

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    Reason for 2 of 2
    ?
    • 1.The eliminative asymptote toward convergence presupposes that competing hypotheses share a common likelihood function, which is not guaranteed across incommensurable models.
      ?

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    • 2.Keynes and Carnap both demonstrated that logical probability requires a determinate measure function over a structured language, making imprecision a formal deficiency rather than a permissible feature.
      ?

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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Bayesian evidential support has an eliminative nature
      ?

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    • 2.Bayesian evidential support only needs bounds on the values of comparative plausibility ratios
      ?

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    • 3.These bounds only play a significant role while evidence remains fairly weak
      ?

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